Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning

14 September 2023

Papers citing "Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning"

11 / 11 papers shown

Title
If Concept Bottlenecks are the Question, are Foundation Models the Answer? Nicola Debole Pietro Barbiero Francesco Giannini Andrea Passerini Stefano Teso Emanuele Marconato 56 0 0 28 Apr 2025
Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens Samuele Bortolotti Emanuele Marconato Paolo Morettin Andrea Passerini Stefano Teso 53 2 0 16 Feb 2025
Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations Atticus Geiger Zhengxuan Wu Christopher Potts Thomas F. Icard Noah D. Goodman CML 73 98 0 05 Mar 2023
GlanceNets: Interpretabile, Leak-proof Concept-based Models Emanuele Marconato Andrea Passerini Stefano Teso 96 64 0 31 May 2022
Post-hoc Concept Bottleneck Models Mert Yuksekgonul Maggie Wang James Y. Zou 133 182 0 31 May 2022
Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations Wolfgang Stammer Marius Memmel P. Schramowski Kristian Kersting 76 25 0 04 Dec 2021
Coherent Hierarchical Multi-Label Classification Networks Eleonora Giunchiglia Thomas Lukasiewicz AILaw 24 94 0 20 Oct 2020
Conditional Gaussian Distribution Learning for Open Set Recognition Xin Sun Zhen Yang Chi Zhang Guohao Peng K. Ling BDL UQCV 133 214 0 19 Mar 2020
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 171 311 0 07 Feb 2020
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan Ö. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 120 293 0 17 Oct 2019
Logic Tensor Networks for Semantic Image Interpretation Ivan Donadello Luciano Serafini Artur Garcez 51 208 0 24 May 2017